# BEDSR-Net-Reimplementation

**Repository Path**: xsdf1985/BEDSR-Net-Reimplementation

## Basic Information

- **Project Name**: BEDSR-Net-Reimplementation
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-10-14
- **Last Updated**: 2023-10-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# BEDSR-Net

This repository is unofficial implementation of [BEDSR-Net: A Deep Shadow Removal Network From a Single Document Image](https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_BEDSR-Net_A_Deep_Shadow_Removal_Network_From_a_Single_Document_CVPR_2020_paper.html) [Lin+, CVPR 2020] with PyTorch.

A refined version of [IsHYuhi's implementation](https://github.com/IsHYuhi/BEDSR-Net_A_Deep_Shadow_Removal_Network_from_a_Single_Document_Image).

## Fix several problems
1. nn.ConvTranspose2d compatible with higher version of Pytorch
2. gradcam uses too much vram, use [pytorch-grad-cam](https://github.com/jacobgil/pytorch-grad-cam) instead
3. provide default correct training config 
4. provide easy inference code

## Dependencies
Pytorch, torchvision, matplotlib, wandb, albumentations, pytorch-grad-cam

## Dataset Structure

The dataset should be formatted like below, train.csv and test.csv can be generated using 

```python
python make_dataset.py
```
```
.
├── csv/
│   └── Jung/
│       ├── train.csv
│       └── test.csv
└── dataset/
    └── Jung/
        ├── train/
        │   ├── input/
        │   │   ├── *.jpg
        │   │   └── ...
        │   └── target/
        │       ├── *.jpg
        │       └── ...
        └── test/
            ├── input/
            │   ├── *.jpg
            │   └── ...
            └── target/
                ├── *.jpg
                └── ...
```


## Training

Training BE-Net
```python
python3 train_benet.py ./configs/model\=benet/config.yaml
```

Training BEDSR-Net
```python
python3 train_bedsrnet.py ./configs/model\=bedsrnet/config.yaml
```

You can use W&B by ```--use_wandb```.

## Infer

mask sure put all your model state_dict into pretrained directory

```python
python infer.py
```

result images will be produced in results folder